Outcome prediction

ABSTRACT

Outcome predictions based on historical data relating to events in a “game” (which can be a sports context, an e-sports contest, a business situation, a medical situation, or other scenarios) can be generated using a watcher process that receives event data and triggers conversion of a “different” last event received into arguments for one or more analysis protocols, which are executed against a data model created by an analysis engine. Methods, systems, articles of manufacture, and the like are described.

CROSS-REFERENCE TO RELATED APPLICATION

The current application claims priority under 35 U.S.C. § 119(e) to U.S.provisional application Ser. No. 62/152,687 filed Apr. 24, 2015 andunder 35 U.S.C. § 120 to Patent Cooperation Treaty application serialno. PCT/US2016/029089, the disclosures of which are incorporated hereinby reference in its entirety.

TECHNICAL FIELD

The subject matter described herein relates to data analysis and tooutcome prediction based on data analysis.

BACKGROUND

Data analysis can be a powerful tool for making predictions aboutpossible outcomes in situations in which historical data can beeffectively leveraged in light of recent events to identify outcomeprobabilities for one or more possible outcomes. Possible (butnon-limiting) applications for a tool capable of making these outcomepredictions in an efficient and accurate manner include sports outcomes,business outcomes, medical outcomes, etc. Currently available approachesto data analysis based outcome prediction are generally not accessiblewithout significant expertise in data science and/or computerprogramming.

SUMMARY

In one aspect, a method can include initiating a watcher process tocorrespond to a designated game for which event data representative of aplurality of events occurring in the designated game are generated,converting the event data for a most recently received event of theplurality of events into one or more arguments for one or more analysisprotocols, and executing the one or more analysis protocols against adata model created by an analysis engine. The watcher process receivesthe event data, which includes events pertaining to the designated game,from an event data source. The converting occurs when the most recentlyreceived event received by the watcher process differs from a previouslyreceived event. The executing includes the analysis engine calculatingan adjusted probability of at least one outcome relevant to thedesignated game. The calculating reflects the event data for the mostrecently received event. The method can further include notifying aclient application server to update a last event cache accessible byclient application executing on a client machine to display an outcomeprediction based on the adjusted probability.

In some variations one or more of the following features can optionallybe included in any feasible combination. The analysis engine cancalculate the adjusted probability using a tag overlap analysis method.The watcher process can track the plurality of events occurring duringthe designated game. The data source can include an external eventapplication programming interface. The receiving of the event data caninclude at least one of the watcher process pulling the event data fromthe event data source, the watcher process receiving the event datapushed to it by the event data source, and the event data being receivedby the watcher process according to a preset schedule. The method canfurther include submitting, by a game daemon to game database, new eventinformation comprising at least one of information about the mostrecently received event and analysis results comprising the adjustedprobability. The designated game can include at least one of a sportscontest and a baseball game.

Implementations of the current subject matter can include, but are notlimited to, methods consistent with the descriptions provided herein aswell as articles that comprise a tangibly embodied machine-readablemedium operable to cause one or more machines (e.g., computers, etc.) toresult in operations implementing one or more of the described features.Similarly, computer systems are also described that may include one ormore programmable processors and one or more memories coupled to the oneor more processors. A memory, which can include a non-transitorycomputer-readable or machine-readable storage medium, may include,encode, store, or the like one or more programs that cause one or moreprocessors to perform one or more of the operations described herein.Computer implemented methods consistent with one or more implementationsof the current subject matter can be implemented by one or more dataprocessors residing in a single computing system or in multiplecomputing systems. Such multiple computing systems can be connected andcan exchange data and/or commands or other instructions or the like viaone or more connections, including but not limited to a connection overa communication network (e.g. the Internet, a wireless wide areanetwork, a local area network, a wide area network, a wired network, orthe like), via a direct connection between one or more of the multiplecomputing systems, over a shared bus, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to outcomeprediction in a sports context, it should be readily understood thatsuch features are not intended to be limiting. For example, outcomepredictions consistent with the approaches described herein can beapplicable to many situations in which historical data are available.Such situations can include physical sports, electronic sports (e.g.video games), gambling on physical and/or electronic sports, medicaltreatments and/or health outcomes, investment decisions, businessoutcomes, etc., among others. The claims that follow this disclosure areintended to define the scope of the protected subject matter.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 shows a diagram illustrating aspects of a system showing featuresconsistent with implementations of the current subject matter;

FIG. 2 shows a process flow diagram illustrating aspects of a methodhaving one or more features consistent with implementations of thecurrent subject matter; and

FIG. 3, FIG. 4, FIG. 5, and FIG. 6 show screenshots illustratingfeatures consistent with implementations of the current subject matter.

When practical, similar reference numbers denote similar structures,features, or elements.

DETAILED DESCRIPTION

Currently available approaches to data analysis and predictive modelinggenerally lack a capability to process potentially large historical datasets in a manner that allows outcome predictions to be generated andpresented to a user quickly (even for very large historical data sets)and without the user needing a detailed knowledge or expertise in dataanalysis techniques, programming, etc. Various features described hereinmay provide advantages over previously available approaches. Suchadvantages may include, but are not necessarily limited to, improvementsin usability (e.g. user experience), greater efficiency in use ofcomputing resources (e.g. bandwidth, memory, processor cycles, etc.),and the like.

FIG. 1 shows a multi-layer architecture diagram of a prediction system100 consistent with implementations of the current subject matter. Theprediction system 100 includes a backend layer 102, an application layer104, and a client layer 106. The backend layer 102 receives data inputs110, which can include one or more historical data sources 112 and anevent data source 114, which can, in some examples, be an eventapplication program interface (API). In an example related to outcomepredictions for a baseball game, a historical data source 112 is abaseball related historical data source, and an event data source 114 isa baseball event API. The backend layer 102 also includes an analysisengine 120, which accesses the historical data source 112 to make aseries of calculations based on a data model, as well as a predictionhandler daemon 122. It will be understood that the backend layer 102,the application layer 104, and the client layer 106 can optionally beimplemented all on a single computing system or alternatively with oneor more components implemented on different computing systems. In anexample in which implementation occurs on multiple computing systems,discrete systems or groups of systems can be assigned to functions ofthe various layers. Alternatively, the components of the layers 102,104, 106 need not be segregated in this manner (e.g. two or more systemscan include components of the backend layer 102 and the applicationlayer 104, etc.).

In an example consistent with FIG. 1, in which the prediction system 100is used for sports-related predictions, and more specifically forbaseball-related predictions, the prediction handler daemon 122 is agame daemon 122. Every night during a baseball season (or at some otherrelevant interval), the game daemon 122 pulls or otherwise accesses aschedule of upcoming games (e.g. games scheduled for the next day) andlaunches a watcher process 124 for each upcoming game. The launching caninvolve starting up (initiating, instantiating, etc.) a thread or othercomputational process on one or more computer systems and/or one or moreprogrammable processors. Each watcher process 124 advantageously startsup before its game is scheduled to begin. Once it is started, thewatcher process 124 pulls event data about its watched game from theevent data source 114 at some relevant interval. The analysis engine 120and the prediction daemon can exchange analysis protocols 126 andanalysis results 130 as discussed in more detail below.

In the application layer 104, a client application server 140 includes alast event cache 142. The client application server 140 can be incommunication with a user database 144, which can include a user table146 and a user attribute table 150, and also with a game database 152,which can include an analysis table 154, a play-by-play (PBP) table 156,and a game table 160 as discussed in more detail below.

The client layer 106 can include authentication functionality 170 aswell as a component or components for requesting information aboutcurrent or upcoming games (today's games request 172), a last event 174,and a last prediction 176 from the client application server 140 asdiscussed in more detail below. In some examples, the client layer 106can be part of or otherwise associated with a television or other videobroadcast of a sporting event such as a baseball game. For example, thepredictions discussed herein can be shown via one or more graphicaloverlays upon or windows embedded within a video stream showing abaseball game or other sporting event.

FIG. 2 shows a process flow chart 200 illustrating features of a methodconsistent with implementations of the current subject matter. At 205, awatcher process 124 is launched (e.g. initiated, instantiated, etc.) tocorrespond to a designated game. The watcher process 124 performs tasksrelating to tracking events occurring in the game for which the watcherprocess 124 is launched.

During the game (which as noted elsewhere herein, can broadly refer tosome collection of events for which relevant historical data areavailable and for which outcome predictions are desired), at 210, thegame watcher process receives event data pertaining to the designatedgame from an event data source. The event data source in some examplescan be an external event API 114, and the receiving of event data canoccur as part of a pull operation (e.g. pulling the event data fromevent data source driven by periodic requests from the watcher process124), a push operation (e.g. the watcher process receiving the eventdata pushed to it by the event data source), according to a presetschedule, or the like. The watcher process 124 can advantageously pullevent data from the event data source 114 on regularly or irregularlyspaced time periods, which can have a duration that is relevant to theparameters of the designated game (e.g. every 5 seconds, etc.). Arelevant duration can be defined as a period having a sufficiently shortduration to ensure capture of a newly generated quantum of event dataprior to a next event in the designated game for which a prediction canbe generated but also of sufficiently long duration to avoid multiplecalls to the event data source 114 for which no new event data has beengenerated.

In the illustrative example of a baseball game, the period can be on theorder of one to a few seconds such that event data regarding a mostrecent pitch and any other relevant game events (e.g. a change inlocations or numbers of base runners due to a stolen base or a caughtstealing or pickoff, etc.) can be received in time to be reflected in aprediction for a next pitch. In some examples, a push operation can beadvantageous for supplying event data to a watcher process 114, assumingthat the event data source 114 is configured appropriately. In anexample in which the event data source 114 is a third party (e.g.external) application programming interface (API), a data query enginecan be used as an intermediary such that the data query engine accessesthe third party API via a pull operation with some set frequency (e.g.every second) and in turn pushes relevant data to each of one or moreconcurrently running watcher processes 124. Alternatively, a call fornew event from the watcher process 124, a push of new event data fromthe event data source 114, and/or a scheduling of a later delivery ofevent data can be triggered or otherwise determined by previouslyreceived event data. For example, in a baseball context, receipt by thewatcher process 124 of event data indicating a last out of an inning cancause the watcher process to delay a next call for a pull of event datafor an expected time period before a next inning begins (e.g. 2 minutes,etc.).

At 215, when a most recently received event received by the watcherprocess 124 differs from a previously received event (e.g. because anadditional, new event has occurred during the designated game), thewatcher process 124 converts the most recently event received into oneor more arguments for one or more analysis protocols 126 (e.g. baseballanalysis protocols, soccer analysis protocols, etc.) and then runs theanalysis protocols 126 against the analysis engine 120. The analysisengine 120 converts each analysis protocol 126 into one or morealgorithms, which it runs against the historical data received from thehistorical data source 112 to generate analysis results 130, which arereturned to the prediction daemon 122.

At 220, the one or more analysis protocols are executed against a datamodel created by the analysis engine 120. The analysis engine 120calculates adjusted probabilities of outcomes given the situationalvariables of the current game state. In a example in which thedesignated game is a baseball game, outcomes for which adjustedprobabilities can be calculated can include one or more of pitchoutcomes, at-bat outcomes, scoring outcomes, game result outcomes, etc.In another example in which the designated game is soccer, outcomes forwhich adjusted probabilities can be calculated can include one or moreof which player will score next, likelihood of a penalty kick beingsuccessful, a probability of a goal being scored within a certain amountof time, which team is likely to score next, etc. In yet another examplein which the designated game is American football, the outcomes forwhich adjusted probabilities can be calculated can include one or moreof which player will score next, whether a field goal will be made ormissed, which team will score next, whether a next play will be arunning play or a passing play, whether a penalty will be called on thenext play, etc.

In some examples, an analysis engine 120 can include a statisticsengine, which can optionally be implemented via a cloud-based service,or on dedicated machines. The analysis engine 120 can be based on one ormore programming models, such as for example MapReduce or the like, andcan be capable of calculating analysis statistics in parallel.Additionally, the analysis engine 120 can include modules for differentvariable types. The analysis engine can also calculate enrichmentstatistics and can perform one or more statistical tests, for example,using a tag overlap analysis method. Tag overlap can be characterized asa situation in which tagged values overlap with the members of a dataset n more than would be expected by random chance. For numericvariables, this inquiry can involve determining whether the datadistributions are significantly different, for example using a T-test,or one or more non-parametric tests. In some implementations of thecurrent subject matter, the analysis engine 120 can implement one ormore machine learning methods.

The analysis engine 120 is configured based on data from the historicaldata source 112. In the example of a baseball game event prediction, thehistorical data source 112, which includes multivariate data relevant tothe predictions being made. The analysis engine 120 converts the one ormore analysis protocols 126, runs the algorithm, and returns theanalysis results 130 to the prediction daemon 122. In this manner, thehistorical database is converted to a smaller footprint via variouscompression techniques.

The historical data source 112 can generally include data from one ormore databases or other sources that is relevant to outcome predictionsfor the type of game being analyzed. For example, for a baseballprediction implementation, the historical data source 112 can includeone or more databases storing baseball historical data, such as forexample player and action outcomes for batters and pitchers withrelevant situational information (e.g., the count, number of baserunnersand/or outs, current score, inning, other game situation data, specificbatter vs. specific hitter outcomes, umpire characteristics, weather,ballpark characteristics, etc.). In a soccer example, the historicaldata source 112 can include one or more databases storing soccerhistorical data, such as for example player and action outcomes forplayers at different positions with relevant situational information(e.g., remaining game time, weather conditions, type of defense beingplayed, outcomes against similar opposing teams or players, outcomesagainst a given goalie or goalies with similar styles or othercharacteristics, referee characteristics, current score, etc.). In anon-sports example (e.g. medical outcomes), the historical data source112 can include one or more databases storing relevant historical data,such as for example patient and treatment variable outcomes forpatients, caregivers, treatment approaches, etc. with relevantsituational information (e.g., illness state at the start of atreatment, patient age and general health assessment, other treatmentsor pharmaceuticals recently experienced by the patient, experience levelof the caregiver, type of medical setting, etc.).

At 225, when the prediction daemon 122 has new event information and/oranalysis results 130 about a game, the prediction daemon 122 submitsthat information to the appropriate tables in the game database 152 andsends a notification to the client application server 142 to update itslast event cache 142 containing latest game and event information.

A user can begin using the prediction system 100 by logging in, or on afirst use, by registering for a login identity. These operations can becompleted via an authentication module 170 or other similarfunctionality executing on a client machine, which can be any kind ofcomputing device (e.g. a personal or laptop computer, a mobile devicesuch as a phone or tablet, etc.). FIG. 3 shows a screenshot 300illustrating an example of a login screen. Logging in can occur via oneor more of a Web interface (e.g. accessed via a Web browser executing ona computing device), a dedicated application (e.g. a standalone programseparate from a browser such as an “app” running on a computing device),an applet (e.g. a program executed within a browser, such as aJavascript applet), etc. The client application server 140 processes thelogin request, and authenticates the user for access to the features ofthe prediction system 100. The client application server 140 can thenupdate the login/registration information in the user database 144.

When a user is logged in, a game panel can be displayed to the user viaa display device on the client machine at 230. The screenshot 400 ofFIG. 4 shows an example of a game panel, which can show a listing orother visual representation of the games that are being played currentlyor that are scheduled to be played that day, as well as informationabout those games (e.g. whether or not a game has started, is inprogress, or has already ended, the scheduled start time for a game, theteam logos, the venue, the records of the teams playing in the game,etc.). For a game that is in progress or already ended, the game panelcan display typical box score information, such as for example anin-progress or final score, key individual or team statistics, etc. Asnoted above, the client layer can supply display data regarding apredicted situational batting average or other relevant outcomepredictions relating to a game such that these measures are displayed inconjunction with a video stream showing the game and are dynamicallyupdated according to changes in current game state as reflected by eventdata about the designated game.

Upon receipt by the client machine of a user choice of a game from thegame panel, a pitch prediction panel is displayed at 235. The user'sgame choice can be recorded in a user attributes table 150 in the userdatabase 144, which can optionally be stored at the application layer104. FIG. 5 and FIG. 6 show screenshots 600, 700 illustrating an exampleof a pitch prediction panel consistent with implementations of thecurrent subject matter. In the baseball-related example illustrated inthe screenshots of FIG. 3 through FIG. 6, the pitch prediction panel candisplay the latest information for a single game, such as for examplethe current state of the game (e.g. one or more of the team that iscurrently at bat, the inning, the number of outs, the number of runnerson base, the pitcher name and whether the pitcher throws right or lefthanded, the batter name and whether the batter bats right or lefthanded, the count of balls and strikes, a player image such as athumbnail picture of the pitcher and/or batter, etc.), as well as thepredictions made by the analysis engine 120 about the probability ofwhat will happen next. FIG. 5 shows the pitch prediction panel for afirst game situation with a 2 ball, 2 strike count and 2 outs in thebottom of the 8^(th) inning, and FIG. 6 shows the pitch prediction panelfor a second game situation a pitch later, with the count now 3 ballsand 2 strikes. The batter's predicted situational batting average forthe current situation is updated from 0.355 in FIG. 5 to 0.225 in FIG. 6to reflect predictions by the analysis engine 120.

In some implementations of the current subject matter, requests forpredictions can be limited to coming from authenticated users of theprediction system 100. After competing the authentication process, theclient layer 106 can receive an authentication key. The client layer 106can use the authentication key to query the client application server140. Queries can include getting information about all games today, thelast event for a given game, and the last prediction made by the systemfor a given game.

The examples presented herein are discussed in relation to animplementation for providing outcome predictions in association with thesport of baseball. It will be readily understood that similar approachescan be applied both to other sports and as to other situations unrelatedto sports in which data-driven predictions have value. For example, useof the term “game” should be interpreted to refer to any kind ofsporting event (e.g. a match, a contest, a meet, a competition, etc.Additionally, in a non-sports example, “game” can refer to an event orset of events during or for which data-driven predictions are made.

In some implementations of the current subject matter, outcomepredictions generated using one or more of the features described hereincan be used for generating odds for gambling transactions relating to“proposition bets” that may be made during the course of a game. Forexample, a prediction that a most likely next player to score a goal ina soccer match can include a probability of this outcome. Odds for aproposition wager regarding which player will score next can offer apayout of somewhat less than this statistical probability such that the“house” (e.g. the wagering establishment) receives a profit on eachwager. For example, if the probability of player X being the next toscore a goal is calculated using the above-noted techniques to be 20%, awager can be offered in which a gambler makes a wager for $1 which, ifsuccessful, would pay the gambler back $4 (a 4 to 1 payoff rather thanthe 5 to 1 payoff that would be indicated by the 20% probability).

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject mattermay be described for illustrative purposes in relation to an contentresource management system, an enterprise resource software system, orother business software solution or architecture, it should be readilyunderstood that such features are not intended to be limiting.

Implementations of the current subject matter can include, but are notlimited to, methods consistent with the descriptions provided above aswell as articles that comprise a tangibly embodied machine-readablemedium operable to cause one or more machines (e.g., computers, etc.) toresult in operations implementing one or more of the described features.Similarly, computer systems are also described that may include one ormore processors and one or more memories coupled to the one or moreprocessors. A memory, which can include a computer-readable storagemedium, may include, encode, store, or the like one or more programsthat cause one or more processors to perform one or more of theoperations described herein. Computer implemented methods consistentwith one or more implementations of the current subject matter can beimplemented by one or more data processors residing in a singlecomputing system or multiple computing systems. Such multiple computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it used, such a phrase is intendedto mean any of the listed elements or features individually or any ofthe recited elements or features in combination with any of the otherrecited elements or features. For example, the phrases “at least one ofA and B;” “one or more of A and B;” and “A and/or B” are each intendedto mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” Use of the term “based on,” above and in theclaims is intended to mean, “based at least in part on,” such that anunrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A computer program product comprising anon-transitory machine-readable medium storing instructions that areconfigured to, when executed by at least one programmable processor,cause the at least one programmable processor to perform operationscomprising: initiating a watcher process to correspond to a designatedgame; based at least on the initiating of the watcher process,automatically pulling, from an event data source via the watcherprocess, event data, the event data representative of a plurality ofevents occurring in the designated game, the automatic pulling the eventdata from the event data source comprising (i) periodically generating arequest for the event data; and (ii) causing transmitting of the requestfor the event data to the event source, a frequency for the periodicgenerating of the request for the event data selected based at least inpart on a relevant parameter of the designated game such that new eventdata is generated between a first time of transmitting a first requestfor the event data and a second time of transmitting a second requestfor the event data; determining an event type associated with a mostrecently pulled event; setting the frequency for the periodic generatingof the request for the event data based on the event type; convertingthe event data for the most recently pulled event of the plurality ofevents into one or more arguments for one or more analysis protocols,the converting occurring based at least in part on the most recentlypulled event pulled by the watcher process being new relative to apreviously pulled event; executing the one or more analysis protocolsagainst a data model created by an analysis engine, the executingcomprising the analysis engine predicting an adjusted probability of atleast one outcome of an event within the designated game, the predictingreflecting the event data for the most recently pulled event; andnotifying a client application server to update a last event cacheaccessible by client application executing on a client machine todisplay an outcome prediction of the event within the designated gamebased on the adjusted probability.
 2. The computer program product ofclaim 1, wherein the analysis engine predicting the adjusted probabilitycomprises using a tag overlap analysis method.
 3. The computer programproduct of claim 1, wherein the operations further comprise tracking,via the watcher process, the plurality of events occurring during thedesignated game.
 4. The computer program product of claim 1, wherein thedata source comprises an external event application programminginterface.
 5. The computer program product of claim 1, wherein theoperations further comprise submitting, by a game daemon to gamedatabase, new event information comprising at least one of informationabout the most recently pulled event or analysis results comprising theadjusted probability.
 6. The computer program product of claim 1,wherein the designated game comprises at least one of a sports contestor a baseball game.
 7. The computer program product of claim 1, whereinthe designated game comprises a baseball game, and wherein the outcomeprediction of the event within the designated game comprises at leastone of a pitch outcome, an at-bat outcome, or a scoring outcome.
 8. Thecomputer program product of claim 1, wherein the designated gamecomprises a soccer game, and wherein the outcome prediction of the eventwithin the designated game comprises at least one of an identity of aplayer most likely to score next, a likelihood of a penalty kick beingsuccessful, a probability of a goal being scored within an amount oftime, or an identity of a team most likely to score next.
 9. Thecomputer program product of claim 1, wherein the designated gamecomprises an American football game, and wherein the outcome predictionof the event within the designated game comprises at least one of anidentity of a player most likely to score next, a likelihood of a fieldgoal being successful, an identity of a team most likely to score next,or whether a next play comprises a running play or a passing play.
 10. Asystem comprising: computer hardware configured to execute at least onecomputer program comprising a plurality of computerized instructions,the plurality of computerized instructions configured to, when executedby the computer hardware, cause the computer hardware to performoperations comprising: initiating a watcher process to correspond to adesignated game; based at least on the initiating of the watcherprocess, automatically pulling, from an event data source via thewatcher process, event data, the event data representative of aplurality of events occurring in the designated game; converting theevent data for a most recently pulled event of the plurality of eventsinto one or more arguments for one or more analysis protocols, theconverting occurring based at least in part on the most recently pulledevent pulled by the watcher process differs from a previously pulledevent; executing the one or more analysis protocols against a data modelcreated by an analysis engine, the executing comprising the analysisengine predicting an adjusted probability of at least one outcome of anevent within the designated game, the predicting the adjustedprobability comprising a tag overlap analysis method, the adjustedprobability reflecting the event data for the most recently pulledevent; and notifying a client application server to update a last eventcache accessible by client application executing on a client machine todisplay an outcome prediction of the event within the designated gamebased on the adjusted probability.
 11. The system of claim 10, whereinthe operations further comprise tracking, via the watcher process, theplurality of events occurring during the designated game.
 12. The systemof claim 10, wherein the data source comprises an external eventapplication programming interface.
 13. The system of claim 10, whereinthe operations further comprise submitting, by a game daemon to gamedatabase, new event information comprising at least one of informationabout the most recently pulled event or analysis results comprising theadjusted probability.
 14. The system of claim 10, wherein the designatedgame comprises at least one of a sports contest or a baseball game. 15.A computer-implemented method comprising: initiating a watcher processto correspond to a designated game; based at least on the initiating ofthe watcher process, automatically pulling, from an event data sourcevia the watcher process, event data, the event data representative of aplurality of events occurring in the designated game; converting theevent data for a most recently received event of the plurality of eventsinto one or more arguments for one or more analysis protocols, theconverting occurring based at least in part on the most recently pulledevent pulled by the watcher process being new relative to a previouslyreceived event; executing the one or more analysis protocols against adata model created by an analysis engine, the executing comprising theanalysis engine predicting an adjusted probability of at least oneoutcome of an event within the designated game, the predictingcomprising a tag overlap method, the adjusted probability reflecting theevent data for the most recently pulled event; and notifying a clientapplication server to update a last event cache accessible by clientapplication executing on a client machine to display an outcomeprediction of the event within the designated game based on the adjustedprobability.
 16. The computer-implemented method of claim 15, furthercomprising tracking, via the watcher process, the plurality of eventsoccurring during the designated game.
 17. The computer-implementedmethod of claim 15, wherein the data source comprises an external eventapplication programming interface.
 18. The computer-implemented methodof claim 15, further comprising submitting, by a game daemon to gamedatabase, new event information comprising at least one of informationabout the most recently pulled event or analysis results comprising theadjusted probability.
 19. A computer program product comprising anon-transitory machine-readable medium storing instructions that areconfigured to, when executed by at least one programmable processor,cause the at least one programmable processor to perform operationscomprising: initiating a watcher process to correspond to a designatedgame; based at least on the initiating of the watcher process,automatically pulling, from an event data source via the watcherprocess, event data, the event data representative of a plurality ofevents occurring in the designated game; converting the event data for amost recently pulled event of the plurality of events into one or morearguments for one or more analysis protocols, the converting occurringbased at least in part on the most recently pulled event pulled by thewatcher process being new relative to a previously pulled event;executing the one or more analysis protocols against a data modelcreated by an analysis engine, the executing comprising the analysisengine predicting an adjusted probability of at least one outcome of anevent within the designated game, the predicting comprising a tagoverlap method, the adjusted probability reflecting the event data forthe most recently pulled event; and notifying a client applicationserver to update a last event cache accessible by client applicationexecuting on a client machine to display an outcome prediction of theevent within the designated game based on the adjusted probability.